A method includes operating equipment to consume energy resources including energy or power purchased from a utility, and obtaining a block-and-index rate profile for a future time period. The block-and-index rate profile includes a block rate and a block size for each of a plurality of sub-periods in the future time period. The block size for a sub-period identifies an amount of energy or power priced at the block rate for the sub-period. The method also includes applying the block-and-index rate profile in an optimization process for the equipment over the time period, running the optimization process, and allocating energy resources to the equipment over the time period in accordance with a result of the optimization process.
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1. A system for allocating energy resources to equipment that operate to serve one or more buildings, the system comprising: equipment operable to consume one or more energy resources including energy or power purchased from a utility to serve one or more buildings; one or more processors; and non-transitory computer-readable media communicably coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the one or more processors to: obtain a block-and-index rate profile for a future time period, the block-and-index rate profile comprising a block rate and a block size for each of a plurality of sub-periods in the future time period, the block size for a sub-period identifying an amount of the energy or power priced at the block rate for the sub-period; apply the block-and-index rate profile as an input to an optimization process for the equipment over the future time period; run the optimization process to determine an amount of each of the one or more energy resources to be consumed or produced by the equipment at each of a plurality of time steps within the future time period; and allocate the one or more energy resources to the equipment over the future time period in accordance with a result of the optimization process.
Energy management for buildings. This invention addresses the problem of efficiently allocating energy resources to equipment serving buildings, particularly when purchasing energy from a utility with complex pricing structures. The system includes equipment that consumes energy resources, such as electricity purchased from a utility, to operate and serve one or more buildings. It also comprises processors and computer-readable media containing instructions. When executed, these instructions enable the processors to: 1. Obtain a block-and-index rate profile for a future time period. This profile defines a block rate and a block size for multiple sub-periods within that future time. The block size specifies the quantity of energy priced at the block rate for each sub-period. 2. Use this block-and-index rate profile as an input for an optimization process applied to the equipment's operation over the future time period. 3. Execute the optimization process to determine the optimal amount of each energy resource to be consumed or produced by the equipment at various time steps within the future period. 4. Allocate the energy resources to the equipment over the future time period based on the outcomes of the optimization process.
2. The system of claim 1 , wherein the instructions cause the one or more processors to obtain the block-and-index rate profile for the future time period by: generating a graphical user interface configured to prompt a user to input the block-and-index rate profile; and receiving user input of the block size and the block rate for each of the plurality of sub-periods.
This invention relates to a system for managing data processing operations, specifically focusing on optimizing block-and-index rate profiles for future time periods. The system addresses the challenge of efficiently configuring data processing tasks by allowing users to define custom block-and-index parameters for different sub-periods within a future time frame. The core functionality involves generating a graphical user interface (GUI) that prompts users to input specific block sizes and block rates for each sub-period. The system then processes these inputs to create a block-and-index rate profile tailored to the user's requirements. This profile can be used to optimize data processing workflows, ensuring that resources are allocated efficiently based on anticipated workload demands. The GUI serves as an interactive tool, enabling users to dynamically adjust parameters to achieve desired performance outcomes. By allowing manual configuration of block sizes and rates, the system provides flexibility in adapting to varying processing needs, improving overall system efficiency and responsiveness. The invention is particularly useful in environments where data processing tasks require precise control over resource allocation to meet specific performance targets.
3. The system of claim 1 , wherein the block-and-index rate profile comprises an index rate for each of the plurality of time steps in the future time period.
A system for managing data storage and retrieval in a distributed computing environment addresses the challenge of efficiently handling large-scale data operations while optimizing performance and resource utilization. The system includes a block-and-index rate profile that dynamically adjusts data storage and indexing operations over a future time period. This profile specifies an index rate for each time step within the future period, allowing the system to balance between storing data in blocks and creating indexes to facilitate faster retrieval. The indexing process involves generating metadata structures that map data locations, enabling quick access to stored information. The block storage mechanism organizes data into contiguous blocks, optimizing storage efficiency and reducing overhead. By dynamically adjusting the index rate for each time step, the system adapts to varying workload demands, ensuring optimal performance under different operational conditions. This approach enhances scalability and reliability in distributed storage systems, particularly in environments with fluctuating data access patterns. The system may also include mechanisms for monitoring performance metrics and adjusting the block-and-index rate profile in real-time to maintain efficiency. The overall solution improves data management in large-scale computing environments by dynamically balancing storage and indexing operations based on predicted future demands.
4. The system of claim 3 , wherein the instructions cause the one or more processors to obtain the block-and-index rate profile for the future time period by receiving a spreadsheet file uploaded by a user, the spreadsheet file defining the index rate for each of the plurality of time steps.
This invention relates to a system for managing financial instruments, specifically focusing on optimizing the allocation of assets between fixed-rate and index-rate blocks over time. The system addresses the challenge of dynamically adjusting investment strategies to balance risk and return based on changing market conditions. The system includes a processor that executes instructions to generate a block-and-index rate profile for a future time period, which defines how assets should be allocated between fixed-rate and index-rate blocks at each time step. The profile is obtained by receiving a spreadsheet file uploaded by a user, where the spreadsheet specifies the index rate for each time step. The system also includes a user interface for displaying the profile and a data storage component for storing the profile. The system may further include a simulation module to evaluate the impact of different allocation strategies on investment performance. The invention aims to provide flexibility in financial planning by allowing users to define custom rate profiles and assess their potential outcomes.
5. The system of claim 3 , wherein the instructions cause the one or more processors to obtain the block-and-index rate profile for the future time period by generating a prediction of the index rate for each of the plurality of time steps in the future time period.
This invention relates to systems for managing financial transactions, specifically optimizing block-and-index rate profiles for future time periods. The system addresses the challenge of predicting and adjusting transaction rates to improve efficiency and reduce costs in financial networks. The system includes one or more processors and memory storing instructions that, when executed, perform several functions. The system generates a prediction of the index rate for each time step within a future time period, creating a block-and-index rate profile. This profile is used to optimize transaction processing by dynamically adjusting rates based on predicted market conditions. The system also monitors transaction data and network conditions to refine predictions and ensure accurate rate adjustments. By predicting index rates for future time steps, the system enables proactive management of transaction costs and network performance, improving overall financial transaction efficiency. The invention may be integrated into existing financial networks or blockchain systems to enhance rate prediction and optimization capabilities.
6. The system of claim 1 , wherein the instructions cause the one or more processors to obtain the block-and-index rate profile for the future time period by performing an optimization to determine the block size for each of the plurality of sub-periods.
A system for optimizing data storage and retrieval in a distributed ledger or blockchain environment addresses inefficiencies in block size management, which can lead to network congestion, slow transaction processing, or excessive resource consumption. The system dynamically adjusts block sizes over time to balance throughput, latency, and resource utilization. It generates a block-and-index rate profile for a future time period by performing an optimization process that determines the optimal block size for each of multiple sub-periods within that time frame. The optimization considers factors such as network load, transaction volume, and computational constraints to maximize efficiency. The system may also include components for monitoring network conditions, predicting future demand, and adjusting block sizes in real-time or near-real-time. This approach ensures that the blockchain operates at peak performance under varying conditions, reducing bottlenecks and improving scalability. The optimization process may involve mathematical modeling, machine learning, or heuristic algorithms to derive the most effective block sizes for each sub-period. The system may further integrate with consensus mechanisms to ensure that block size adjustments align with network protocols and security requirements.
7. The system of claim 1 , wherein the instructions cause the one or more processors to generate a graphical representation of the block-and-index rate profile for the future time period and cause the graphical representation to be displayed to a user.
This invention relates to a system for visualizing block-and-index rate profiles over future time periods. The system addresses the challenge of effectively presenting complex data trends to users, particularly in scenarios where understanding future performance metrics is critical. The system includes one or more processors and memory storing instructions that, when executed, enable the system to generate a graphical representation of a block-and-index rate profile for a specified future time period. The graphical representation is then displayed to a user, allowing them to analyze and interpret the data visually. The system may also include components for collecting, processing, and storing the data used to generate these profiles, ensuring accurate and timely visualization. By providing a clear and intuitive graphical output, the system helps users make informed decisions based on projected performance trends. The invention is particularly useful in fields such as financial analysis, network performance monitoring, or any domain where tracking and predicting rate-based metrics is essential. The graphical representation may include various visual elements, such as charts, graphs, or interactive displays, to enhance user understanding and engagement with the data.
8. A method, comprising: operating equipment to consume one or more energy resources including energy or power purchased from a utility; obtaining a block-and-index rate profile for a future time period, the block-and-index rate profile comprising a block rate and a block size for each of a plurality of sub-periods in the future time period, the block size for a sub-period identifying an amount of energy or power priced at the block rate for the sub-period; applying the block-and-index rate profile in an optimization process for the equipment over the future time period; running the optimization process to determine an amount of each of the one or more energy resources to be consumed or produced by the equipment at each of a plurality of time steps within the future time period; and allocating the one or more energy resources to the equipment over the future time period in accordance with a result of the optimization process.
This invention relates to energy management systems for optimizing the consumption or production of energy resources, including utility-purchased energy or power, to minimize costs or improve efficiency. The problem addressed is the need to efficiently allocate energy resources over a future time period, considering variable pricing structures such as block-and-index rate profiles. A block-and-index rate profile defines a block rate (a fixed price) and a block size (a fixed amount of energy or power) for each sub-period within a future time period. The block size determines how much energy is priced at the block rate before switching to an index rate (a variable rate tied to market conditions). The method involves operating equipment that consumes or produces energy resources, obtaining a block-and-index rate profile for a future time period, and applying this profile in an optimization process. The optimization process determines the optimal amount of each energy resource to be consumed or produced at multiple time steps within the future time period. The results of this optimization are then used to allocate the energy resources to the equipment, ensuring cost-effective or efficient operation. The optimization may account for constraints such as equipment capacity, operational limits, or energy storage availability. This approach helps balance energy costs and operational efficiency by dynamically adjusting resource allocation based on predicted pricing structures.
9. The method of claim 8 , wherein obtaining the block-and-index rate profile for the future time period comprises: generating a graphical user interface configured to prompt a user to input the block-and-index rate profile; and receiving user input of the block size and the block rate for each of the plurality of sub-periods.
This invention relates to systems for managing data storage and retrieval, specifically optimizing block storage and indexing operations over time. The problem addressed is the need for flexible, user-configurable block storage strategies that adapt to varying workload demands across different time periods. Traditional systems often use fixed block sizes and rates, which can lead to inefficiencies in storage utilization and retrieval performance. The invention provides a method for dynamically adjusting block storage parameters based on user-defined profiles. A graphical user interface (GUI) is generated to allow users to input a block-and-index rate profile for a future time period. The profile includes specifications for block size and block rate for each of multiple sub-periods within the time period. The GUI prompts the user to define these parameters, and the system receives the input, enabling customization of storage behavior according to anticipated workload patterns. This allows for optimized storage allocation and indexing performance tailored to specific operational needs, improving efficiency and adaptability in data management systems. The method ensures that storage resources are allocated dynamically, reducing waste and enhancing retrieval speed based on user-defined criteria.
10. The method of claim 8 , wherein the block-and-index rate profile comprises an index rate for each of the plurality of time steps in the future time period.
A method for optimizing data storage and retrieval in a distributed system involves managing block-and-index rate profiles to balance storage efficiency and access speed. The method addresses the challenge of efficiently storing large datasets while ensuring fast retrieval, particularly in systems where data is distributed across multiple nodes. The block-and-index rate profile defines how data is partitioned into blocks and indexed over a future time period, with each time step having a specific index rate. This allows the system to dynamically adjust the balance between storage space and retrieval performance based on predicted future access patterns. The method includes generating a block-and-index rate profile that specifies the index rate for each time step within the future time period, ensuring that the system can adapt to changing data access requirements. By optimizing the index rate for each time step, the method improves overall system efficiency by reducing unnecessary indexing while maintaining fast data retrieval when needed. This approach is particularly useful in distributed storage systems where data access patterns vary over time, and efficient resource allocation is critical.
11. The method of claim 10 , wherein obtaining the block-and-index rate profile comprises receiving a spreadsheet file uploaded by a user, the spreadsheet file defining the index rate for each of the plurality of time steps.
A method for managing block-and-index rates in a financial or operational system involves obtaining a block-and-index rate profile, which defines the index rate for each of a plurality of time steps. The profile is acquired by receiving a spreadsheet file uploaded by a user, where the spreadsheet contains the index rate values for each time step. This allows for flexible and user-defined rate adjustments over time. The method may also include generating a block-and-index rate profile based on user inputs, such as a base rate and a spread, and applying this profile to financial instruments or operational processes. The system may further validate the profile to ensure compliance with predefined rules or constraints. The method supports dynamic rate adjustments, enabling users to modify rates as needed while maintaining system integrity. This approach is useful in financial modeling, loan processing, or other applications where time-based rate adjustments are required. The spreadsheet-based input method simplifies user interaction by leveraging familiar tools for data entry and management.
12. The method of claim 10 , wherein obtaining the block-and-index rate profile comprises generating a prediction of the index rate for each of the plurality of time steps in the future time period.
This invention relates to predictive modeling for block-and-index rate profiling in data processing systems. The problem addressed is the need to accurately forecast index rates over future time periods to optimize resource allocation and performance in systems handling large-scale data operations. The method involves generating a prediction of the index rate for each time step within a specified future time period. This prediction is part of a broader process that includes obtaining a block-and-index rate profile, which characterizes the expected workload distribution between block operations and index operations. The prediction model analyzes historical data and current system metrics to estimate future index rates, enabling dynamic adjustment of system resources to maintain efficiency. The method also includes determining a block rate for each time step, which, combined with the index rate prediction, forms the complete block-and-index rate profile. This profile is used to guide scheduling, resource allocation, and load balancing in data processing environments. The invention improves system performance by anticipating workload patterns and preemptively adjusting system parameters to handle expected demands. The prediction process may employ machine learning techniques, statistical models, or other analytical methods to derive accurate index rate forecasts. The system dynamically updates these predictions as new data becomes available, ensuring continuous optimization of performance. This approach is particularly useful in high-throughput environments where efficient indexing is critical for maintaining system responsiveness and reliability.
13. The method of claim 8 , wherein obtaining the block-and-index rate profile comprises performing an optimization to determine the block size for each of the plurality of sub-periods.
This invention relates to optimizing data storage and retrieval in a distributed system, particularly for handling variable workloads. The problem addressed is inefficient resource utilization when fixed block sizes are used, leading to suboptimal performance in terms of throughput, latency, or storage efficiency. The solution involves dynamically adjusting block sizes and indexing strategies based on workload characteristics over multiple sub-periods. The method includes analyzing workload patterns to generate a block-and-index rate profile, which defines optimal block sizes for different sub-periods. This profile is determined through an optimization process that evaluates trade-offs between block size, indexing overhead, and system performance metrics. The optimization considers factors such as data access frequency, update patterns, and system constraints to balance storage efficiency and retrieval speed. By dynamically adjusting block sizes, the system adapts to changing workloads, improving overall efficiency. The method also involves applying the generated profile to allocate and manage storage blocks, ensuring that each sub-period uses the most suitable block size for its workload. This dynamic approach reduces wasted space from oversized blocks or excessive fragmentation from undersized blocks, while maintaining efficient indexing for fast data retrieval. The optimization process may use mathematical models, machine learning, or heuristic techniques to determine the best block sizes for each sub-period. The result is a more adaptive and efficient storage system that handles varying workloads effectively.
14. The method of claim 8 , comprising displaying a graphical representation of the block-and-index rate profile over the future time period to a user.
A system and method for visualizing block-and-index rate profiles over time. The technology addresses the challenge of monitoring and predicting data processing rates in distributed systems, where understanding the relationship between block processing and indexing operations is critical for performance optimization. The method involves generating a block-and-index rate profile, which quantifies the rate at which data blocks are processed and indexed over a specified future time period. This profile is then displayed as a graphical representation to a user, allowing for intuitive analysis of trends, bottlenecks, or inefficiencies in the system. The graphical display may include time-series data, comparative metrics, or predictive models to aid in decision-making. By visualizing these rates, users can identify patterns, forecast resource needs, and optimize system performance. The method integrates with existing data processing frameworks to provide real-time or historical insights, supporting both reactive troubleshooting and proactive planning. The graphical representation enhances user comprehension by translating complex rate data into an accessible visual format, facilitating better resource allocation and system tuning.
15. One or more non-transitory computer readable media containing program instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising: obtaining a block-and-index rate profile for energy or power to be purchased from a utility and consumed by building equipment during a future time period, the block-and-index rate profile comprising a block rate and a block size for each of a plurality of sub-periods in the future time period, the block size for a sub-period identifying an amount of energy or power priced at the block rate for the sub-period during the sub-period; applying the block-and-index rate profile in an optimization process for the equipment over the time period; running the optimization process to determine an amount of each of one or more energy resources to be consumed or produced by the equipment at each of a plurality of time steps within the future time period; and allocating the one or more energy resources to the equipment over the future time period in accordance with a result of the optimization process.
The invention relates to energy management systems for optimizing the consumption and production of energy in buildings using block-and-index rate profiles. The problem addressed is the need to efficiently manage energy costs and usage in buildings by leveraging utility rate structures that combine fixed block rates with variable index rates. The system obtains a block-and-index rate profile for energy or power, which includes a block rate and block size for each sub-period within a future time period. The block size specifies the amount of energy or power priced at the block rate during that sub-period. The system applies this rate profile in an optimization process to determine the optimal consumption or production of energy resources by building equipment at various time steps. The optimization process evaluates different energy resources, such as electricity, natural gas, or renewable sources, and allocates them to the equipment based on the results. This approach ensures cost-effective energy management by aligning consumption with favorable rate structures while meeting operational demands. The system dynamically adjusts energy allocation to minimize costs and improve efficiency over the specified time period.
16. The non-transitory computer-readable media of claim 15 , wherein obtaining the block-and-index rate profile for the future time period comprises: generating a graphical user interface configured to prompt a user to input the block-and-index rate profile; and receiving user input of the block size and the block rate for each of the plurality of sub-periods.
This invention relates to systems for managing data storage and retrieval, specifically optimizing block storage operations by dynamically adjusting block sizes and rates over time. The problem addressed is the inefficiency of static block storage configurations, which fail to adapt to varying workload demands, leading to suboptimal performance and resource utilization. The invention provides a method for generating a block-and-index rate profile for a future time period, which defines block sizes and block rates for multiple sub-periods within that time. The profile is obtained by generating a graphical user interface that prompts a user to input the block size and block rate for each sub-period. The user provides these values, allowing the system to dynamically adjust storage operations based on anticipated workload patterns. This approach enables more efficient data handling by aligning block storage parameters with expected demand fluctuations, improving performance and resource allocation. The system may also include additional features, such as generating a block-and-index rate profile based on historical data or predictive analytics, and applying the profile to optimize storage operations in real-time. The invention ensures that block storage configurations are tailored to specific time-based requirements, reducing latency and maximizing throughput.
17. The non-transitory computer-readable media of claim 15 , wherein the block-and-index rate profile comprises an index rate for each of the plurality of time steps in the future time period.
The invention relates to data storage systems, specifically optimizing write operations in storage devices by dynamically adjusting block-and-index rates. The problem addressed is inefficient storage performance due to fixed or suboptimal write strategies, leading to bottlenecks in data handling. The system involves a storage controller that manages write operations by generating a block-and-index rate profile for a future time period. This profile defines the rate at which data blocks and corresponding indexes are written at each time step within the period. The profile is generated based on predicted storage demands, ensuring that write operations are distributed efficiently to prevent overloads and maintain performance. The storage controller then executes the write operations according to this profile, dynamically adjusting the rates to adapt to changing conditions. The block-and-index rate profile includes specific index rates for each time step, allowing precise control over index updates. This ensures that index structures remain synchronized with data blocks, improving retrieval efficiency. The system may also incorporate historical data and real-time monitoring to refine future profiles, enhancing adaptability. The overall approach aims to balance write throughput and resource utilization, particularly in high-demand storage environments.
18. The non-transitory computer-readable media of claim 17 , wherein obtaining the block-and-index rate profile comprises receiving a spreadsheet file uploaded by a user, the spreadsheet file defining the index rate for each of the plurality of time steps.
A system and method for managing data storage and retrieval in a distributed computing environment addresses the challenge of efficiently organizing and accessing large datasets across multiple storage nodes. The invention provides a dynamic indexing mechanism that adjusts the frequency of index updates based on time-based profiles, optimizing performance and resource utilization. The system generates a block-and-index rate profile, which specifies the rate at which data blocks and corresponding indexes are processed over a series of time steps. This profile can be customized by users through a spreadsheet file, where each time step is assigned a specific index rate. The system then applies this profile to control the indexing process, ensuring that indexes are updated at the defined rates during each time step. This approach allows for flexible and adaptive indexing strategies, improving data retrieval efficiency while minimizing computational overhead. The invention also includes mechanisms for distributing the indexing tasks across multiple nodes in a distributed system, ensuring scalability and fault tolerance. By dynamically adjusting the indexing rate based on predefined profiles, the system optimizes storage and retrieval operations in large-scale data environments.
19. The non-transitory computer-readable media of claim 17 , wherein obtaining the block-and-index rate profile comprises generating a prediction of the index rate for each of the plurality of time steps in the future time period.
The invention relates to data storage systems, specifically optimizing the allocation of storage resources by predicting future data indexing needs. The problem addressed is inefficient resource allocation in storage systems, where static or reactive approaches fail to anticipate varying workload demands, leading to performance bottlenecks or underutilized capacity. The system generates a block-and-index rate profile, which predicts the rate at which data blocks will be written and indexed over a future time period. This profile is divided into multiple time steps, each with an estimated index rate. The prediction is based on historical data patterns, workload trends, or other predictive models. By forecasting these rates, the system can dynamically allocate storage and indexing resources to match anticipated demand, improving efficiency and performance. The invention also includes methods for adjusting storage operations based on the predicted profile, such as pre-allocating storage space or prioritizing indexing tasks. This proactive approach ensures that resources are available when needed, reducing latency and preventing system overload. The system may also adapt to real-time changes by continuously updating the predictions as new data becomes available. The technical solution is implemented using non-transitory computer-readable media, storing instructions that, when executed, perform the predictive modeling and resource allocation tasks. The focus is on anticipating future workloads to optimize storage system performance dynamically.
20. The non-transitory computer-readable media of claim 15 , wherein obtaining the block-and-index rate profile comprises performing an optimization to determine the block size for each of the plurality of sub-periods.
This invention relates to optimizing data storage and retrieval in computer systems, particularly for managing variable block sizes in storage operations. The problem addressed is the inefficiency in traditional storage systems that use fixed block sizes, which can lead to wasted storage space or excessive fragmentation. The solution involves dynamically adjusting block sizes based on data characteristics and access patterns to improve storage efficiency and performance. The system obtains a block-and-index rate profile, which defines optimal block sizes for different sub-periods of a storage operation. This profile is generated by performing an optimization process that evaluates data patterns, access frequencies, and storage constraints to determine the most efficient block size for each sub-period. The optimization considers factors such as data locality, write/read frequencies, and storage medium characteristics to minimize overhead and maximize throughput. By dynamically adjusting block sizes, the system reduces fragmentation, improves storage density, and enhances retrieval speeds. The method can be applied to various storage media, including solid-state drives, hard disk drives, and distributed storage systems. The optimization process may involve mathematical modeling, machine learning, or heuristic algorithms to derive the optimal block-and-index rate profile. The system then uses this profile to allocate and manage storage blocks dynamically, ensuring efficient use of storage resources.
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April 17, 2019
March 29, 2022
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